Artificial intelligence techniques to support cognitive rehabilitation
In recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/197546 |
| Acceso en línea: | http://hdl.handle.net/10261/197546 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Acquired brain injury Decision support Classifiers Cognitive rehabilitation |
| Sumario: | In recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing patient profiles and their assigned tasks. The distribution of percentage of effort does not perfectly match the distribution of the cognitive profile. This paper provides a thorough analysis of the patient profiles, showing that a patient’s initial profile and the task execution scores during their first few sessions can be used to better predict their final improvement, to a certain degree of accuracy. Furthermore, results show that more executions of tasks does not automatically lead to improvement. Practice does not seem to make perfect. The proposed technique involves the incorporation of task-weights in the new scheduler. |
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